Testing and Modeling Dependencies Between a Network and Nodal Attributes

• 成果类型: Article
• 署名作者: Fosdick, Bailey K.; Hoff, Peter D.
• 署名单位: Colorado State University System; Colorado State University Fort Collins; University of Washington; University of Washington Seattle
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2015.1008697
• 发表日期: 2015
• 页码: 1047-1056
• 关键词: round-robin analysis; social-influence; markov graphs; BEHAVIOR; selection; variance; obesity

摘要

Network analysis is often focused on characterizing the dependencies between network relations and node-level attributes. Potential relationships are typically explored by modeling the network as a function of the nodal attributes or by modeling the attributes as a function of the network. These methods require specification of the exact nature of the association between the network and attributes, reduce the network data to a small number of summary statistics, and are unable to provide predictions simultaneously for missing attribute and network information. Existing methods that model the attributes and network jointly also assume the data are fully observed. In this article, we introduce a unified approach to analysis that addresses these shortcomings. We use a previously developed latent variable model to obtain a low-dimensional representation of the network in terms of node-specific network factors. We introduce a novel testing procedure to determine if dependencies exist between the network factors and attributes as a surrogate for a test of dependence between the network and attributes. We also present a joint model for the network relations and attributes, for use if the hypothesis of independence is rejected, which can capture a variety of dependence patterns and be used to make inference and predictions for missing observations.